Fetching the paper…
Reading the bibliography…
The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications.
J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. Säckinger, and R. Shah, “Signature verification using a ”siamese” time delay neural network,” International Journal of Pattern Recognition and Artificial Intelligence
1993
Earlier work this paper cites.
C. J. Leggetter and P. C. Woodland, “Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models,” Computer Speech and Language
1995
Earlier work this paper cites.
Y. Freund and R. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences
1997
Earlier work this paper cites.
R. Caruana, “Multitask learning: A knowledge-based source of inductive bias,” Machine Learning
1997
Earlier work this paper cites.
A. Edelman, T. A. Arias, and S. T. Smith, “The geometry of algorithms with orthogonality constraints,” Journal of Matrix Analysis and Applications
1998
Earlier work this paper cites.
Cambridge University Press, 1998
L. Bottou, Online Algorithms and Stochastic Approximations · 1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
T. Joachims, “Transductive inference for text classification using support vector machines,” in International Conference on Machine Learning (ICML)
1999
Earlier work this paper cites.
D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, “Speaker verification using adapted Gaussian Mixture Models,” Digital Signal Processing
2000
Earlier work this paper cites.
H. Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of Statistical Planning and Inference
2000
Earlier work this paper cites.
Y. Chen, G. Wang, and S. Dong, “Learning with progressive transductive support vector machine,” Pattern Recognition Letters
2003
Earlier work this paper cites.
A. Vinokourov, N. Cristianini, and J. Shawe-Taylor, “Inferring a semantic representation of text via cross-language correlation analysis,” in Annual Conference on Neural Information Processing Systems (NIPS)
2003
Earlier work this paper cites.
C. Andrieu, N. Freitas, A. Doucet, and M. Jordan, “An introduction to mcmc for machine learning,” Machine Learning
2003
Earlier work this paper cites.
K. Murphy, A. Torralba, and W. Freeman, “Using the forest to see the trees: a graphical model relating features, objects, and scenes,” in Annual Conference on Neural Information Processing Systems (NIPS)
2003
Earlier work this paper cites.
B. Zadrozny, “Learning and evaluating classifiers under sample selection bias,” in International Conference on Machine Learning (ICML)
2004
Earlier work this paper cites.
D. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonical correlation analysis: An overview with application to learning methods,” Neurocomputing
2004
Earlier work this paper cites.
T. Evgeniou and M. Pontil, “Regularized multi-task learning,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2004
Earlier work this paper cites.
M. Fink, “Object classification from a single example utilizing class relevance pseudo-metrics,” in Annual Conference on Neural Information Processing Systems (NIPS)
2004
Earlier work this paper cites.
M. Dudík, R. E. Schapire, and S. J. Phillips, “Correcting sample selection bias in maximum entropy density estimation,” in Annual Conference on Neural Information Processing Systems (NIPS)
2005
Earlier work this paper cites.
C. Ding, T. Li, W. Peng, and H. Park, “Orthogonal nonnegative matrix tri-factorizations for clustering,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2005
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2005
Earlier work this paper cites.
C. Rosenberg, M. Hebert, and H. Schneiderman, “Semisupervised self-training of object detection models,” in Workshops on Application of Computer Vision (WACV/MOTION)
2005
Earlier work this paper cites.
O. Javed, S. Ali, and M. Shah, “Online detection and classification of moving objects using progressively improving detectors,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2005
Earlier work this paper cites.
E. Bart and S. Ullman, “Cross-generalization: Learning novel classes from a single example by feature replacement,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2005
Earlier work this paper cites.
X. Liao, Y. Xue, and L. Carin, “Logistic regression with an auxiliary data source,” in International Conference on Machine Learning (ICML)
2005
Earlier work this paper cites.
R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, “Learning object categories from google’s image search,” in IEEE International Conference on Computer Vision (ICCV)
2005
Earlier work this paper cites.
H. Daumé III and D. Marcu, “Domain adaptation for statistical classifiers,” Journal of Artificial Intelligence Research
2006
Earlier work this paper cites.
K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola, “Integrating structured biological data by kernel maximum mean discrepancy,” Bioinformatics
2006
Earlier work this paper cites.
D. L. Donoho, “Compressed sensing,” Transactions on Information Theory
2006
Earlier work this paper cites.
A. Agarwal and B. Triggs, “A local basis representation for estimating human pose from cluttered images,” in Asian Conference on Computer Vision (ACCV)
2006
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “One-shot learning of object categories,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2006
Earlier work this paper cites.
MIT Press, 2006
O. Chapelle, B. Schölkopf, and A. Zien, Semi-supervised learning · 2006
Earlier work this paper cites.
J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Schölkopf, “Correcting sample selection bias by unlabeled data,” in Annual Conference on Neural Information Processing Systems (NIPS)
2007
Earlier work this paper cites.
W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Boosting for transfer learning,” in International Conference on Machine Learning (ICML)
2007
Earlier work this paper cites.
J. Yang, R. Yan, and A. G. Hauptmann, “Cross-domain video concept detection using adaptive SVMs,” in ACM Multimedia
2007
Earlier work this paper cites.
H. Cheng, P.-N. Tan, and R. Jin, “Localized support vector machine and its efficient algorithm,” in SIAM International Conference on Data Mining (SDM)
2007
Earlier work this paper cites.
J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Information-theoretic metric learning,” in International Conference on Machine Learning (ICML)
2007
Earlier work this paper cites.
G. Taylor, A. Chosak, and P. Brewer, “OVVV: Using virtual worlds to design and evaluate surveillance systems,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2007
Earlier work this paper cites.
B. Wu and R. Nevatia, “Improving part based object detection by unsupervised, online boosting,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2007
Earlier work this paper cites.
R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng, “Self-taught learning: transfer learning from unlabeled data,” in International Conference on Machine Learning (ICML)
2007
Earlier work this paper cites.
V. Ferrari and A. Zisserman, “Learning visual attributes.,” in Annual Conference on Neural Information Processing Systems (NIPS)
2007
Earlier work this paper cites.
Y. Chan and H. Ng, “Domain adaptation with active learning for word sense disambiguation,” in Annual Meeting of the Association for Computational Linguistics(ACL)
2007
Earlier work this paper cites.
PhD thesis, Hebrew University, 7 2007
S. Shalev-Shwartz, Online Learning: Theory, Algorithms, and Applications · 2007
Earlier work this paper cites.
F. Schroff, A. Criminisi, and A. Zisserman, “Harvesting image databases from the web,” in IEEE International Conference on Computer Vision (ICCV)
2007
Earlier work this paper cites.
W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Self-taught clustering,” in International Conference on Machine Learning (ICML)
2008
Earlier work this paper cites.
Z. Whang, Y. Song, and C. Zhang, “Transferred dimensionality reduction,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
2008
Earlier work this paper cites.
M. Sugiyama, S. Nakajima, H. Kashima, P. v. Buenau, and M. Kawanabe, “Direct importance estimation with model selection and its application to covariate shift adaptation,” in Annual Conference on Neural Information Processing Systems (NIPS)
2008
Earlier work this paper cites.
W. Jiang, E. Zavesky, S.-F. Chang, and A. Loui, “Cross-domain learning methods for high-level visual concept classification,” in International Conference on Image Processing (ICIP)
2008
Earlier work this paper cites.
A. Singh, P. Singh, and G. Gordon, “Relational learning via collective matrix factorization,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in International Conference on Machine Learning (ICML)
2008
Earlier work this paper cites.
C. Zhang, R. Hammid, and Z. Zhang, “Taylor expansion based classifier adaptation: Application to person detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2008
Earlier work this paper cites.
X. Shi, W. Fan, and J. Ren, “Actively transfer domain knowledge,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
2008
Earlier work this paper cites.
X.-J. Wang, L. Zhang, X. Li, and W.-Y. Ma, “Annotating images by mining image search results,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2008
Earlier work this paper cites.
T. Kanamori, S. Hido, and M. Sugiyama, “Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection.,” Journal of Machine Learning Research
2009
Earlier work this paper cites.
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf, “Covariate shift by kernel mean matching,” in Dataset Shift in Machine Learning
2009
Earlier work this paper cites.
L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank, “Domain transfer SVM for video concept detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2009
Earlier work this paper cites.
H. Daumé III, “Frustratingly easy domain adaptation,” CoRR
2009
Earlier work this paper cites.
E. Zhong, W. Fan, J. Peng, K. Zhang, J. Ren, D. Turaga, and O. Verscheure, “Cross domain distribution adaptation via kernel mapping,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2009
Earlier work this paper cites.
Z.-J. Zha, t. Mei, M. Wang, Z. Wang, and X.-S. Hua, “Robust distance metric learning with auxiliary knowledge,” in AAAI International Joint Conference on Artificial Intelligence (IJCAI)
2009
Earlier work this paper cites.
K. Chaudhuri, S. Kakade, K. Livescu, and K. S. Sridharan, “Multi-view clustering via canonical correlation analysis,” in International Conference on Machine Learning (ICML)
2009
Earlier work this paper cites.
P. M. Roth, S. Sternig, H. Grabner, and H. Bischof, “Classifier grids for robust adaptive object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2009
Earlier work this paper cites.
S. Stalder, H. Grabner, and L. V. Gool, “Exploring context to learn scene specific object detectors.,” in International Workshop on Performance Evaluation of Tracking and Surveillance (PETS)
2009
Earlier work this paper cites.
P. Dollár, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: a benchmark,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2009
Earlier work this paper cites.
Y. Mansour, M. Mohri, and A. Rostamizadeh, “Domain adaptation with multiple sources,” in Annual Conference on Neural Information Processing Systems (NIPS)
2009
Earlier work this paper cites.
L. Duan, I. W. Tsang, D. Xu, and T.-S. Chua, “Domain adaptation from multiple sources via auxiliary classifiers,” in International Conference on Machine Learning (ICML)
2009
Earlier work this paper cites.
T. Tommasi and B. Caputo, “The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories,” in BMVA British Machine Vision Conference (BMVC)
2009
Earlier work this paper cites.
C. H. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2009
Earlier work this paper cites.
M. Palatucci, D. Pomerleau, G. Hinton, and T. M. Mitchell, “Zero-shot learning with semantic output codes,” in Annual Conference on Neural Information Processing Systems (NIPS)
2009
Earlier work this paper cites.
Morgan & Claypool Publishers, 2009
X. Zhu, A. Goldberg, R. Brachman, and T. Dietterich, Introduction to semi-supervised learning · 2009
Earlier work this paper cites.
T. Kamishima, M. Hamasaki, and S. Akaho, “Trbagg: A simple transfer learning method and its application to personalization in collaborative tagging,” in IEEE International Conference on Data Mining (ICDM)
2009
Earlier work this paper cites.
E. Rodner and J. Denzler, “Learning with few examples by transferring feature relevance,” in BMVA British Machine Vision Conference (BMVC)
2009
Earlier work this paper cites.
Q. Yang, Y. Chen, G.-R. Xue, W. Dai, and Y. Yong, “Heterogeneous transfer learning for image clustering via the socialweb,” in Annual Meeting of the Association for Computational Linguistics(ACL)
2009
Earlier work this paper cites.
J. Ah-Pine, M. Bressan, S. Clinchant, G. Csurka, Y. Hoppenot, and J.-M. Renders, “Crossing textual and visual content in different application scenarios,” Multimedia Tools and Applications
2009
Earlier work this paper cites.
S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” in International Conference on World Wide Web (WWW)
2010
Earlier work this paper cites.
P. Prettenhofer and B. Stein, “Cross-language text classification using structural correspondence learning,” in Annual Meeting of the Association for Computational Linguistics(ACL)
2010
Earlier work this paper cites.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European Conference on Computer Vision (ECCV)
2010
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” Transactions on Knowledge and Data Engineering
2010
Earlier work this paper cites.
L. Bruzzone and M. Marconcini, “Domain adaptation problems: A dasvm classification technique and a circular validation strategy,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2010
Earlier work this paper cites.
Y. Yao and G. Doretto, “Boosting for transfer learning with multiple sources,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010
Earlier work this paper cites.
T. Tommasi and B. Caputo, “Safety in numbers: learning categories from few examples with multi model knowledge transfer,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010
Earlier work this paper cites.
R. Socher and F.-F. Li, “Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010
Earlier work this paper cites.
X. Shi, Q. Liu, W. Fan, P. S. Yu, and R. Zhu, “Transfer learning on heterogeneous feature spaces via spectral transformation,” in IEEE International Conference on Data Mining (ICDM)
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
M. Zeiler, D. Krishnan, G. Taylor, and R. Fergus, “Deconvolutional networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010
Earlier work this paper cites.
M. Stark, M. Goesele, and B. Schiele, “Back to the future: Learning shape models from 3D CAD data,” in BMVA British Machine Vision Conference (BMVC)
2010
Earlier work this paper cites.
J. Marín, D. Vázquez, D. Gerónimo, and A. López, López, “Learning appearance in virtual scenarios for pedestrian detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010
Earlier work this paper cites.
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2010
Earlier work this paper cites.
E. Miller, N. Matsakis, and P. Viola, “Learning from one example through shared densities on transforms,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010
Earlier work this paper cites.
B. Settles, “”active learning literature survey,” Tech. Rep. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2010
2010
Earlier work this paper cites.
P. Rai, A. Saha, H. Daumé III, and S. Venkatasubramanian, “Domain adaptation meets active learning,” in ACL Workshop on Active Learning for Natural Language Processing (ALNLP)
2010
Earlier work this paper cites.
Y. Zhang and D.-Y. Yeung, “Transfer metric learning by learning task relationships,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2010
Earlier work this paper cites.
J. Weston, S. Bengio, and N. Usunier, “Large scale image annotation: learning to rank with joint word-image embeddings,” Machine Learning
2010
Earlier work this paper cites.
N. Rasiwasia, J. C. Pereira, E. Coviello, G. Doyle, G. R. G. Lanckriet, R. Levy, and N. Vasconcelos, “A new approach to cross-modal multimedia retrieval,” in ACM Multimedia
2010
Earlier work this paper cites.
A. Bergamo and L. Torresani, “Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach,” in Annual Conference on Neural Information Processing Systems (NIPS)
2010
Earlier work this paper cites.
J. Blitzer, S. Kakade, and D. P. Foster, “Domain adaptation with coupled subspaces,” in International Conference on Artificial Intelligence and Statistics (AISTATS)
2011
Cited alongside, same era.
X. Glorot, A. Bordes, and Y. Bengio, “Domain adaptation for large-scale sentiment classification: A deep learning approach,” in International Conference on Machine Learning (ICML)
2011
Cited alongside, same era.
S. J. Pan, J. T. Tsang, Ivor W.and Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” Transactions on Neural Networks
2011
Cited alongside, same era.
M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in IEEE International Conference on Computer Vision (ICCV)
2011
Cited alongside, same era.
A. Sharma and D. W. Jacobs, “Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” in Annual Conference on Neural Information Processing Systems (NIPS)
2014
Later among the works it cites.
E. J. Crowley and A. Zisserman, “The state of the art: Object retrieval in paintings using discriminative regions,” in BMVA British Machine Vision Conference (BMVC)
2014
Later among the works it cites.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Later among the works it cites.
A. Babenko, A. Slesarev, A. Chigorin, and V. S. Lempitsky, “Neural codes for image retrieval,” in European Conference on Computer Vision (ECCV)
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
S. Al-Stouhi and C. K. Reddy, “Adaptive boosting for transfer learning using dynamic updates,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
2011
Cited alongside, same era.
R. Gopalan, R. Li, and R. Chellappa, “Domain adaptation for object recognition: An unsupervised approach,” in IEEE International Conference on Computer Vision (ICCV)
2011
Cited alongside, same era.
B. Kulis, K. Saenko, and T. Darrell, “What you saw is not what you get: Domain adaptation using asymmetric kernel transforms,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2011
Cited alongside, same era.
R. Chattopadhyay, J. Ye, S. Panchanathan, W. Fan, and I. Davidson, “Multi-source domain adaptation and its application to early detection of fatigue,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2011
Cited alongside, same era.
Y. Zhu, Y. Chen, Z. Lu, S. J. Pan, G.-R. Xue, Y. Yu, and Q. Yang, “Heterogeneous transfer learning for image classification,” in AAAI Conference on Artificial Intelligence (AAAI)
2011
Cited alongside, same era.
G.-J. Qi, C. Aggarwal, and T. Huang, “Towards semantic knowledge propagation from text corpus to web images,” in International Conference on World Wide Web (WWW)
2011
Cited alongside, same era.
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in International Conference on Machine Learning (ICML)
2011
Cited alongside, same era.
2014
Later among the works it cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Annual Conference on Neural Information Processing Systems (NIPS)
2014
Later among the works it cites.
D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in Annual Conference on Neural Information Processing Systems (NIPS)
2014
Later among the works it cites.
S. Divvala, A. Farhadi, and C. Guestrin, “Learning everything about anything: Webly-supervised visual concept learning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Later among the works it cites.
B. Sun and K. Saenko, “From virtual to reality: Fast adaptation of virtual object detectors to real domains,” in BMVA British Machine Vision Conference (BMVC)
2014
Later among the works it cites.
L.-C. Chen, S. Fidler, and R. Yuille, Alan L. Urtasun, “Beat the MTurkers: Automatic image labeling from weak 3D supervision,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Later among the works it cites.
D. Vazquez, A. M. López, J. Marín, D. Ponsa, and D. Gerónimo, “Virtual and real world adaptation for pedestrian detection,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2014
Later among the works it cites.
J. Xu, D. Vázquez, A. López, J. Marín, and D. Ponsa, “Learning a part-based pedestrian detector in a virtual world,” Transactions on Intelligent Transportation Systems
2014
Later among the works it cites.
C. L. Zitnick and P. Dollár, “Edge boxes: Locating object proposals from edges,” in European Conference on Computer Vision (ECCV)
2014
Later among the works it cites.
2014
Later among the works it cites.
J. Xu, S. Ramos, D. Vázquez, and A. López, “Domain adaptation of deformable part-based models,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2014
Later among the works it cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Later among the works it cites.
J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko, “LSDA: Large scale detection through adaptation,” in Annual Conference on Neural Information Processing Systems (NIPS)
2014
Later among the works it cites.
Z. Xu, W. Li, L. Niu, and D. Xu, “Exploiting low-rank structure from latent domains for domain generalization,” in European Conference on Computer Vision (ECCV)
2014
Later among the works it cites.
Y. Fu, T. Hospedales, T. Xiang, Z. Fu, and S. Gong, “Transductive multi-view embedding for zero-shot recognition and annotation,” in European Conference on Computer Vision (ECCV)
2014
Later among the works it cites.
R. Layne, T. Hospedales, and S. Gong, “Re-id: Hunting attributes in the wild,” in BMVA British Machine Vision Conference (BMVC)
2014
Later among the works it cites.
Y. Fu, T. Hospedales, T. Xiang, and S. Gong, “Learning multimodal latent attributes,” Transactions of Pattern Recognition and Machine Analyses (PAMI)
2014
Later among the works it cites.
N. Patricia and B. Caputo, “Learning to learn, from transfer learning to domain adaptation: A unifying perspective,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Later among the works it cites.
X. Wang, T.-K. Huang, and J. Schneider, “Active transfer learning under model shift,” in International Conference on Machine Learning (ICML)
2014
Later among the works it cites.
T. Tommasi and T. Tuytelaars, “A testbed for cross-dataset analysis,” in ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2014
Later among the works it cites.
G. Csurka, B. Chidlovskii, and S. Clinchant, “Adapted domain specific class means,” in ICCV workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2015
Later among the works it cites.
G. Matasci, M. Volpi, M. Kanevski, L. Bruzzone, and D. Tuia, “Semi-supervised transfer component analysis for domain adaptation in remote sensing image classification,” Transactions on Geoscience and Remote Sensing
2015
Later among the works it cites.
2015
Later among the works it cites.
R. Aljundi, R. Emonet, D. Muselet, and M. Sebban, “Landmarks-based kernelized subspace alignment for unsupervised domain adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Later among the works it cites.
R. Caseiro, J. F. Henriques, P. Martins, and J. Batista, “Beyond the shortest path : Unsupervised domain adaptation by sampling subspaces along the spline flow,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Later among the works it cites.
W. Wang, R. Arora, K. Livescu, and J. Bilmes, “On deep multi-view representation learning,” in International Conference on Machine Learning (ICML)
2015
Later among the works it cites.
F. Yan and K. Mikolajczyk, “Deep correlation for matching images and text,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Later among the works it cites.
b. Tan, Y. Song, E. Zhong, and Q. Yang, “Transitive transfer learning,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
2015
Later among the works it cites.
L. Yang, L. Jing, J. Yu, and M. K. Ng, “Learning transferred weights from co-occurrence data for heterogeneous transfer learning,” Transactions on Neural Networks and Learning Systems
2015
Later among the works it cites.
M. Xiao and Y. Guo, “Semi-supervised subspace co-projection for multi-class heterogeneous domain adaptation,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
2015
Later among the works it cites.
T. Yao, Y. Pan, C.-W. Ngo, H. Li, and T. Mei, “Semi-supervised domain adaptation with subspace learning for visual recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Later among the works it cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR
2015
Later among the works it cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Later among the works it cites.
M. Ghifary, W. B. Kleijn, M. Zhang, and D. Balduzzi, “Domain generalization for object recognition with multi-task autoencoders,” in IEEE International Conference on Computer Vision (ICCV)
2015
Later among the works it cites.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in International Conference on Machine Learning (ICML)
2015
Later among the works it cites.
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko, “Simultaneous deep transfer across domains and tasks,” in IEEE International Conference on Computer Vision (ICCV)
2015
Later among the works it cites.
X. Shu, G.-J. Qi, J. Tang, and W. Jingdong, “Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation,” in ACM Multimedia
2015
Later among the works it cites.
X. Chen and A. Gupta, “Webly supervised learning of convolutional networks,” in IEEE International Conference on Computer Vision (ICCV)
2015
Later among the works it cites.
P. Panareda-Busto, J. Liebelt, and J. Gall, “Adaptation of synthetic data for coarse-to-fine viewpoint refinement,” in BMVA British Machine Vision Conference (BMVC)
2015
Later among the works it cites.
H. Su, C. Qi, Y. Yi, and L. Guibas, “Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views,” in IEEE International Conference on Computer Vision (ICCV)
2015
Later among the works it cites.
A. Rozantsev, V. Lepetit, and P. Fua, “On rendering synthetic images for training an object detector,” Computer Vision and Image Understanding
2015
Later among the works it cites.
X. Peng, B. Sun, K. Ali, and K. Saenko, “Learning deep object detectors from 3D models,” in IEEE International Conference on Computer Vision (ICCV)
2015
Later among the works it cites.
H. Hattori, V. Naresh Boddeti, K. M. Kitani, and T. Kanade, “Learning scene-specific pedestrian detectors without real data,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Later among the works it cites.
J. Papon and M. Schoeler, “Semantic pose using deep networks trained on synthetic RGB-D,” in IEEE International Conference on Computer Vision (ICCV)
2015
Later among the works it cites.
N. Onkarappa and A. Sappa, “Synthetic sequences and ground-truth flow field generation for algorithm validation,” Multimedia Tools and Applications
2015
Later among the works it cites.
2015
Later among the works it cites.
A. Gaidon and E. Vig, “Online domain adaptation for multi-object tracking,” in BMVA British Machine Vision Conference (BMVC)
2015
Later among the works it cites.
A. Raj, V. P. N. Namboodiri, and T. Tuytelaars, “Subspace alignment based domain adaptation for rcnn detector,” in BMVA British Machine Vision Conference (BMVC)
2015
Later among the works it cites.
Y. Yang and T. M. Hospedales, “A unified perspective on multi-domain and multi-task learning,” in International Conference on Learning representations (ICLR)
2015
Later among the works it cites.
C. Sun, S. Shetty, R. Sukthankar, and R. Nevatia, “Temporal localization of fine-grained actions in videos by domain transfer from web images,” in ACM Multimedia
2015
Later among the works it cites.
L. Castrejón, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba, “Learning aligned cross-modal representations from weakly aligned data,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
S. Saxena and J. Verbeek, “Heterogeneous face recognition with cnns,” in ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2016
Later among the works it cites.
B. Sun, J. Feng, and K. Saenko, “Return of frustratingly easy domain adaptation,” in AAAI Conference on Artificial Intelligence (AAAI)
2016
Later among the works it cites.
B. Chidlovskii, S. Clinchant, and G. Csurka, “Domain adaptation in the absence of source domain data,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
2016
Later among the works it cites.
2016
Later among the works it cites.
K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” Journal of Big Data
2016
Later among the works it cites.
G. Csurka, B. Chidlovskii, S. Clinchant, and S. Michel, “Unsupervised domain adaptation with regularized domain instance denoising,” in ECCV workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2016
Later among the works it cites.
J. Hoffman, S. Gupta, and T. Darrell, “Learning with side information through modality hallucination,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
Y. Yan, Q. Wu, M. Tan, and H. Min, “Online heterogeneous transfer learning by weighted offline and online classifiers,” in ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2016
Later among the works it cites.
2016
Later among the works it cites.
L. Wang, Y. Li, and S. Lazebnik, “Learning deep structure-preserving image-text embeddings,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
B. Chu, V. Madhavan, O. Beijbom, J. Hoffman, and T. Darrell, “Best practices for fine-tuning visual classifiers to new domains,” in ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2016
Later among the works it cites.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in NIPS Workshop on Adversarial Training, (WAT)
2016
Later among the works it cites.
R. Aljundi and T. Tuytelaars, “Lightweight unsupervised domain adaptation by convolutional filter reconstruction,” in ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2016
Later among the works it cites.
B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV)
2016
Later among the works it cites.
M. Long, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” CoRR
2016
Later among the works it cites.
A. Rozantsev, M. Salzmann, and P. Fua, “Beyond sharing weights for deep domain adaptation,” CoRR
2016
Later among the works it cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” Journal of Machine Learning Research
2016
Later among the works it cites.
M.-Y. Liu and O. Tuzel, “Coupled generative adversarial networks,” in Annual Conference on Neural Information Processing Systems (NIPS)
2016
Later among the works it cites.
2016
Later among the works it cites.
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Erhan, and D. Krishnan, “Domain separation networks,” in Annual Conference on Neural Information Processing Systems (NIPS)
2016
Later among the works it cites.
M. Ghifary, W. B. Kleijn, M. Zhang, and D. Balduzzi, “Deep reconstruction-classification networks for unsupervised domain adaptation,” in European Conference on Computer Vision (ECCV)
2016
Later among the works it cites.
W.-Y. Chen, T.-M. H. Hsu, and Y.-H. H. Tsai, “Transfer neural trees for heterogeneous domain adaptation,” in European Conference on Computer Vision (ECCV)
2016
Later among the works it cites.
E. Crowley and A. Zisserman, “The art of detection,” in ECCV Workshop on Computer Vision for Art Analysis, (CVAA)
2016
Later among the works it cites.
F. Massa, B. Russell, and M. Aubry, “Deep exemplar 2D-3D detection by adapting from real to rendered views,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
E. Bochinski, V. Eiselein, and T. Sikora, “Training a convolutional neural network for multi-class object detection using solely virtualworld data,” in IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS)
2016
Later among the works it cites.
G. Ros, L. Sellart, J. Materzyńska, D. Vázquez, and A. López, “The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig, “Virtual worlds as proxy for multi-object tracking analysis,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
S. Richter, V. Vineet, S. Roth, and K. Vladlen, “Playing for data: Ground truth from computer games,” in European Conference on Computer Vision (ECCV)
2016
Later among the works it cites.
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox, “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
A. Shafaei, J. Little, and M. Schmidt, “Play and learn: Using video games to train computer vision models,” in BMVA British Machine Vision Conference (BMVC)
2016
Later among the works it cites.
A. Handa, V. Patraucean, V. Badrinarayanan, S. Stent, and R. Cipolla, “Understanding real world indoor scenes with synthetic data,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
2016
Later among the works it cites.
J. Xu, S. Ramos, D. Vázquez, and A. López, “Hierarchical adaptive structural SVM for domain adaptation,” International Journal of Computer Vision
2016
Later among the works it cites.
C. Gan, T. Yang, and B. Gong, “Learning attributes equals multi-source domain generalization,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Later among the works it cites.
D. Novotny, D. Larlus, and A. Vedaldi, “I have seen enough: Transferring parts across categories,” in BMVA British Machine Vision Conference (BMVC)
2016
Later among the works it cites.
S. Clinchant, G. Csurka, and B. Chidlovskii, “Transductive adaptation of black box predictions,” in Annual Meeting of the Association for Computational Linguistics(ACL)
2016
Later among the works it cites.
C. Gan, C. Sun, L. Duan, and B. Gong, “Webly-supervised video recognition by mutually voting for relevant web images and web video frames,” in European Conference on Computer Vision (ECCV)
2016
Later among the works it cites.